Modeling and Reconstruction of Multi-fiber Population from DWMRICopyright (C) 2008-2009, Bing Jian and Baba C. Vemuri

AboutThis website provides Python modules for modeling and reconstruction of diffusion weighted MRI data. It is a subset of the code internally used in the CVGMI lab at the University of Florida. Three different reconstruction methods are currently included in this program, namely, Mixture of Wisharts (MOW), Diffusion Orientation Transform (DOT) and Q-ball Imaging (QBI).

This program is mainly developed and maintained by Bing Jian, as part of his Ph.D. research, supervised by Prof. Baba Vemuri. For how to obtain the latest source code from this website, please read this page. For more information on how to use this program, please refer to the README file. You can download the source code for free, use and change it as you like, but please refer to this website and cite our TMI and NeuroImage papers.

ReferencesBing Jian and Baba C. Vemuri. A Unified Computational Framework for Deconvolution to Reconstruct Multiple Fibers From Diffusion Weighted MRI. IEEE Trans. Med. Imaging, 26(11):1464-1471, 2007. Bing Jian, Baba C. Vemuri, Evren Özarslan, Paul R. Carney, and Thomas H. Mareci. A novel tensor distribution model for the diffusion weighted MR signal. NeuroImage, 37(1):164-176, 2007. Maxime Descoteaux, Elaine Angelino, Shaun Fitzgibbons, and Rachid Deriche. Regularized, Fast and Robust Analytical Q-Ball Imaging. Magn. Reson. Med., 58:497-510, 2007. Ken E. Sakaie and Mark J. Lowe. An objective method for regularization of fiber orientation distribution derived from diffusion-weighed MRI. NeuroImage, 34:169-176, 2007. Evren Özarslan, Timothy M. Shepherd, Baba C. Vemuri, Stephen J. Blackband, and Thomas H. Mareci. Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). NeuroImage, 36(3):1086-1103, 2006. Maxime Descoteaux, Elaine Angelino, Shaun Fitzgibbons, and Rachid Deriche. Apparent diffusion coefficients from high angular resolution diffusion imaging: Estimation and applications. Magn. Reson. Med., 56(2):395-410, 2006 Adam W. Anderson. Measurement of Fiber Orientation Distributions Using High Angular Resolution Diffusion Imaging. Magn. Reson. Med., 54(5):1194-1206, 2005 Daniel C. Alexander. Multiple-fibre reconstruction algorithms for diffusion MRI. Proc. N.Y. Acad. Sci., 1064:113-133, 2005. Evren Özarslan, Baba C. Vemuri, and Thomas H. Mareci. Generalized scalar measures for diffusion MRI using trace, variance, and entropy. Magn. Reson. Med., 53(4):866-876, 2005. David S. Tuch. Q-ball imaging. Magn. Reson. Med., 52(6):1358-1372, 2004. J.-Donald Tournier, Fernando Calamante, David G. Gadian, and Alan Connelly. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 23(3):1176-1185, 2004. Evren Özarslan and Thomas H. Mareci. Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Magn. Reson. Med., 50(5):955-965, 2003. Daniel C. Alexander, Gareth J. Barker, and Simon R. Arridge. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med., 48(2):331-340, August 2002.

AcknowledgmentThis research was supported by the National Institutes of Health (NIH) Grant EB007082. The simulated data and the IDL visualization program used to generate the picture in this page were provided by Dr. Evren Özarslan. Many thanks go to Santhosh Kodipaka, Angelos Barmpoutis, Ritiwk Kumar and Guang Cheng for testing this program and providing helpful suggestions. Related LinksMICCAI 2008 Diffusion MRI Tutorial: Technology trends and unsolved problems Camino(diffusion MRI toolkit) MRI studio DSI studio CATNAP Tractor ---

Last modified on September 27, 2008Please address any questions, comments to bing.jian@gmail.com